This work has been funded by the Transregional Collaborative Research Centre TRR169 &#34;Crossmodal Learning: Adaptivity, Prediction and Interaction&#34; / German Research Foundation (DFG). The authors thank Stephanie Shields for the helpful comments on the manuscript.

The experiment has been approved by the ethics commission of the Hamburg medical association (PV4509).
1. Participant Selection
<ol>
	<li>Beyond standard selection criteria, such as fitness for pain stimulation, head implants or pre-existing neurological conditions, make sure the participants are not suffering from acute or chronic pain, are not taking any pain medication, and have no known history of substance abuse. Participants should also not have taken part in any pharmacological studies during the 4 weeks prior to the experiment.</li>
	<li>Include participants of any gender, yet take care to only include female participants that are using hormonal contraceptives13,14 to minimize the effect of cyclic changes in pain perception.</li>
	<li>Before administering any kind of stimulation, make sure participants have given informed consent in writing.</li>
</ol>
2. EEG Setup
<ol>
	<li>Select an appropriate cap size and prepare the EEG electrode setup as per the system&#39;s instruction manual.</li>
	<li>Set the sample rate and high/low cutoff as well as the impedance limits of the recording equipment (recommended: 500 Hz, 0.5 Hz high-pass filter, impedances &#60;20 k&#8486;).</li>
	<li>Make sure the stimulation device and the EEG device are not electrically coupled by running the EEG system on battery.</li>
	<li>Ensure that any link between the EEG system and the computer controlling the electrical stimulation device is potential-free.</li>
</ol>
3. Electrical Stimulation Setup
<ol>
	<li>To best make use of the time resolution of the EEG recording, keep the electrical stimulation as short as possible. Set the stimulator to a single, monophasic stimulation pulse with 1 ms duration and 400 V maximum voltage. If a more intense pain level is needed, or the exact timing of the post-stimulus EEG recording does not take precedence, other stimulation protocols can be used.</li>
	<li>Make sure the electrical stimulator is switched on but the output to the electrode is switched off. For the DS7A stimulator the switch labeled &#39;OUTPUT&#39;&#160;to the right of the device should be in the down position.</li>
	<li>Locate the landmark(s) that identify the chosen stimulation site. For a stimulation at the hand, use the muscle between thumb and index finger (abductor/flexor pollicis brevis). Ask the participant to lay their hand on a flat surface with all digits stretched out and apposed. Identify the stimulation site by bisecting the distance between the first knuckles of the thumb and index finger.</li>
	<li>Clean the skin by applying electrode preparation gel. Make sure not to use alcohol or disinfectant, which might leave residue on the skin that can lead to irritation or unreliable stimulation.</li>
	<li>Attach the stimulation electrode and fasten it in place with textile tape.</li>
	<li>Ask the participant to find a comfortable position for the hand and to try not to move the hand during the experiment, if possible. For the participant&#39;s convenience, place a soft tissue under the hand to absorb any humidity, depending on the surface permeability.</li>
	<li>Enable the stimulator&#39;s output by switching the &#39;OUTPUT&#39;&#160;switch to the upward position.</li>
</ol>
4. Determine Starting Points
<ol>
	<li>Instruct the participant on how to operate the rating scale on the screen using the mouse. The left half represents non-painful sensations; the right half corresponds to a standard pain VAS scale, providing a visual equivalent for a range of continuous sensation intensities in the form of a horizontal line. Point out to the participant that the absolute center point of the scale cannot be selected. Provide the participant with the standardized instructions about the anchor points15 (Table 1).</li>
	<li>Give the participant the opportunity to get comfortable with the rating process by applying stimuli of varying intensity and recording the responses. Use the information gathered during this phase to get an estimate for two intensities that consistently evoke strong but nonpainful sensations (low-point) and moderately painful sensations (high-point), respectively. Continue the stimulation for about 25 - 30 trials or until satisfied with having reached good estimates. During this time, it can be beneficial to query the participant for verbal feedback on the intensities and the subjective similarity of repeatedly presented stimulus intensities.</li>
	<li>Try to pick the intensities randomly to evoke responses around the scale center. For best results, do not simply increase or decrease the intensities linearly, and also explore the more extreme ends of the painful side. This phase should also give the participant the opportunity to get accustomed to the potentially unfamiliar stimulation and establish some reference for a consistent rating range. Because of this, it is advisable to apply intensities from the whole range of possible stimulus intensities, while also repeating some intensities.</li>
	<li>Once satisfied with having obtained estimates for both a high-point and low-point starting intensity, inform the participant that the first part of the experiment is about to start and (s)he should keep on rating as practiced while random stimulus intensities are presented.</li>
</ol>
5. Determine Threshold
NOTE: The QUEST algorithm requires some parameters to be specified before starting estimation. Those parameters include the steepness of the psychophysical function (beta, typically 3.5), the fraction of trials where a random answer is expected (delta, typically 0.01), and the fraction of trials where a positive response is expected even though no stimulation is given (gamma, no recommendation). For Bayesian estimation, the range (SD) of the expected ratings and the spacing of possible responses (grain) must be specified. For a VAS, grain should be set to the resolution of the scale (typically 1), and the SD should be set large enough to include both the scale zero point and the maximum possible intensity plus some safety margin. The recommendations and &#34;typical&#34; values given here are explained in detail in the QuestCreate source code included with PsychToolbox6,16. For pain at the threshold, a gamma value of at most 0.01 should be plausible. The estimation method is relatively robust in terms of misspecification of the parameters, however for settings with only few trials, failure to specify sensible parameters might increase the uncertainty of the final estimate. If the standard deviation is set too low, the procedure will have problems converging on estimates that lie outside the area spanned by the standard deviation around the prior guess for the parameter. Thus it is important to rather err on the side of a too large standard deviation.
<ol>
	<li>Create two QUEST sessions with the parameters given above. Start one from the high-point intensity and one from the low-point. Information about the implementation logic of the estimation process can be found in the supplemental material (S1).</li>
	<li>Randomly select a probe intensity of one of the two runs given by the respective QuestMean function.</li>
	<li>Set the electrical stimulator to the probe intensity. If a different intensity than the one suggested by the algorithm needs to be applied or the suggested intensity is out of range, feed the presented intensity back into the QuestUpdate function in step 5.5.</li>
	<li>Trigger the stimulus.</li>
	<li>After the participant has rated the stimulus, run QuestUpdate for the respective estimation session and supply it with the actual stimulus intensity presented as well as the participant&#39;s rating.</li>
	<li>Continue running rating trials until the estimates are stable or a predefined stopping criterion (&#62;40 trials) has been reached.</li>
	<li>Record the mean threshold estimate between both estimation runs, starting from the high and low starting point as given by QuestMean.</li>
	<li>Allow the participant to take a break at this stage, if desired.</li>
</ol>
6. Stimulate at Threshold Level
Note: It is possible to adjust the rating and block count to your needs as long as it is tolerable to the participant.
<ol>
	<li>Inform the participant that for the remaining part of the experiment, more blocks with random stimulation will be following and they should keep rating as they did before. If needed, refresh the instruction on the scale anchor points.</li>
	<li>Start the EEG recording.</li>
	<li>Set the electrical stimulator to the mean threshold estimate obtained in step 5.7 and keep the setting constant throughout the rest of the experiment.</li>
	<li>Start a rating block (30 trials) and observe the data quality of the EEG recording. Depending on the EEG data quality, run 4 - 5 rating blocks and allow the participant to take short breaks in between the blocks. 
	NOTE: Try to keep social interaction with the participant to a minimum during these breaks or standardize the interaction as much as possible.</li>
	<li>When finished, stop the EEG recording, switch the stimulator output to off, and remove the electrode. Debrief the participant after removing the EEG cap.</li>
</ol>

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H., Ehrenstein, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Psychophysical+Methods.">Psychophysical Methods.</a> Mod. Tech. Neurosci. Res. , 1325 (1999).</li><li>Watson, A. B., Pelli, D. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quest:+A+Bayesian+adaptive+psychometric+method.">Quest: A Bayesian adaptive psychometric method.</a> Percept. Psychophys. 33 (2), 113-120 (1983).</li><li>Sims, J. A., Pelli, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+ideal+psychometric+procedure.">The ideal psychometric procedure.</a> Investig. Ophthalmol. Vis. 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Sci. 104 (29), 12187-12192 (2007).</li><li>Gross, J., Schnitzler, A., Timmermann, L., Ploner, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gamma+oscillations+in+human+primary+somatosensory+cortex+reflect+pain+perception.">Gamma oscillations in human primary somatosensory cortex reflect pain perception.</a> PLoS Biol. 5 (5), 1168-1173 (2007).</li><li>Ploner, M., Lee, M. C., Wiech, K., Bingel, U., Tracey, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prestimulus+functional+connectivity+determines+pain+perception+in+humans.">Prestimulus functional connectivity determines pain perception in humans.</a> Proc. Natl. Acad. Sci. U. S. A. 107 (1), 355-360 (2010).</li><li>Oertel, B. G., Preibisch, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Separating+brain+processing+of+pain+from+that+of+stimulus+intensity.">Separating brain processing of pain from that of stimulus intensity.</a> Hum. Brain Mapp. 33 (4), 883-894 (2012).</li><li>Goolkasian, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cyclic+changes+in+pain+perception:+an+ROC+analysis.">Cyclic changes in pain perception: an ROC analysis.</a> Percept. Psychophys. 27 (6), 499-504 (1980).</li><li>Hapidou, E. G., Rollman, G. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Menstrual+cycle+modulation+of+tender+points.">Menstrual cycle modulation of tender points.</a> Pain. 77 (2), 151-161 (1998).</li><li>Huskisson, E. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Measurement+of+pain.">Measurement of pain.</a> Lancet. 304 (7889), 1127-1131 (1974).</li><li>Brainard, D. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Psychophysics+Toolbox.">The Psychophysics Toolbox.</a> Spat. Vis. 10 (4), 433-436 (1997).</li><li>Taesler, P., Rose, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prestimulus+Theta+Oscillations+and+Connectivity+Modulate+Pain+Perception.">Prestimulus Theta Oscillations and Connectivity Modulate Pain Perception.</a> J. Neurosci. 36 (18), 5026-5033 (2016).</li><li>Yarnitsky, D., Sprecher, E., Zaslansky, R., Hemli, J. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multiple+session+experimental+pain+measurement.">Multiple session experimental pain measurement.</a> Pain. 67 (2-3), 327-333 (1996).</li><li>Rosier, E. M., Iadarola, M. J., Coghill, R. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reproducibility+of+pain+measurement+and+pain+perception.">Reproducibility of pain measurement and pain perception.</a> Pain. 98 (1-2), 205-216 (2002).</li><li>Barthelm&#233;, S., Mamassian, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+flexible+Bayesian+method+for+adaptive+measurement+in+psychophysics.">A flexible Bayesian method for adaptive measurement in psychophysics.</a> arXiv:0809.0387. , (2008).</li></ol>

The authors declare that they have no competing financial interests.

Here we used the well theoretically founded QUEST method to efficiently estimate a robust psychophysical threshold between non-pain and pain perception. Using constant stimulation at this threshold enables an analysis of perceptual decisions independent of changes in stimulus magnitude. While we examined threshold intensity at the transition point between innocuous and noxious sensation domains, other points along the pain scale (e.g.,&#160;50 on a 100-point pain scale) can also be anchored with the here presented estimation method. In these cases, care has to be taken to account for habituation or sensitization effects across the course of the experiment. Such effects are more likely to occur for higher stimulation intensities.
One critical step in this procedure is to optimally adjust the necessary parameters for the psychophysical function to be fitted by the adaptive procedure. Another important issue is the instruction given to the participant regarding the anchoring of the response scale. The participant should have a clear understanding of where to range in the subjective intensities on the scale. It is thus very important to standardize and repeat these instructions, whenever necessary, to avoid introducing any bias into the ratings. Specifying a scale that is split into a nonpainful and painful side might prove difficult to handle for some participants, since both sensory continua might differ in their respective sensitivity. In this case, when the information from the split-scale is not needed for further analysis, the estimation procedure can also be carried out as a two-alternative forced choice paradigm. Here, the participant just has to decide, whether a stimulus was perceived as painful or not. In case of problems with the rating scale, the estimation will be robust, as long as the participant&#39;s response about a stimulus being painful or nonpainful is veridical and false responses are within the limits specified by the delta and gamma parameters.
In cases where the initial thresholding does not converge upon a plausible estimate or rating irregularities become evident, the experiment should be interrupted and restarted. In such cases, it might help to ask the participant about their interpretation of the scale and their subjective perception of the stimulation. If technical errors such as a loose electrode or a faulty connection to the stimulator can be ruled out, it might be helpful to ask the participant about their strategies for dealing with pain. Participants who regularly deal with pain in martial arts or high-performance sports, for example, might exhibit irregular responses despite passing the initial screening. Additionally, social interaction with the participant after the beginning of the experiment and during the breaks should be standardized, as not to induce any effects of experimenter demeanor or induced compliance.
The method outlined here has been demonstrated to be very robust within one experimental session. However, there might be substantial differences in thresholds in the same participants when measured over multiple sessions and days. This might in part be due to circadian changes in pain susceptibility or to changes in arousal or motivation. For heat pain, this has also been shown to be an effect of differing interpretation of the scale range over multiple sessions18. These problems could be reduced by re-training participants on the scale anchoring in each session and averaging multiple stimulations into one aggregated rating per trial19. An additional concern is that reattaching the electrode in a different session might not yield the exact same stimulation intensity and thus may change the estimated threshold.
Using an adaptive estimation procedure such as QUEST, in each iteration, the full set of information from all prior thresholding trials is used to determine the optimal intensity for the next test intensity. This decreases the number of necessary trials while increasing the robustness against inconsistent ratings during thresholding compared to classical methods such as the staircase. The thresholding process could be further optimized by independently gathering data in a pilot experiment to better estimate the slope of the psychophysical function for the desired modality or stimulus type7.
Even though the theoretical foundation of the algorithm presented here is sound and we have demonstrated that a robust estimate for exhaustive experiments can be obtained, there are already improved techniques available, that further reduce the number of trials needed to reach robust threshold estimates. These optimized Bayesian methods not only promise less biased results for low trial numbers but also try to fit the position as well as the slope of the psychophysical function in one iteration20.
By using such advanced estimation methods, future research in areas relying on the anchoring of subjective perception can benefit. For one, these algorithms reduce the strain on participants and thus help make the experimental setting more ecologically valid. Additionally, they improve accuracy, not only in threshold experiments, but potentially in all self-report measures suited for psychophysical procedures &#8212; a property especially useful for research in the clinical setting.

When conducting behavioral experiments involving human participants, it is important to be able to closely control the intensities of presented stimuli. Using stimuli of equal intensity for all participants, however, will in some settings introduce the bias of subjective perception. For some perceptual qualities such as pain, there are high inter- and intra-individual variations in perceived intensity at a constant stimulus level1,2. For experiments that assume equal subjective percepts, it is thus a necessity to match the subjectively perceived intensity across participants. This is also important when examining perception at threshold level, e.g., between painful and nonpainful stimulation. Psychophysics research has addressed these kind of problems for decades, and today there are sophisticated but easy-to-use methods available to achieve robust psychophysical anchoring.
A simple, classical method of mapping the intensity of a stimulus to an individual sensation magnitude is the staircase method3. Hereby, the intensity of successive stimuli is increased or decreased, until there is a change in the participant&#39;s response relating to the desired threshold or position on the subjective sensation scale. Repeating this process&#160;number of times, yields a plausible estimate of the reversal point. Classical methods, however, fail to make use of all the information contained in each rating trial. This leads to an unnecessarily high number of trials required to reach convergence. Methods such as (linear) regression or function fitting might fail, if the assumptions for the relationship between stimulus intensity and sensation magnitude are wrong or do not hold for the tested stimulus range. The adaptive procedures not only yield a robust point estimate for a certain subjective intensity, but do so more efficiently. Especially for longer experiments, which heavily rely on accurate estimation of a threshold or sensation magnitude, it is necessary for the psychophysical method to be both robust and at the same time efficient with respect to the number of required trials. This is especially important in fields such as pain research, where the total exposure to painful stimulation should be kept as low as possible for the participants&#39; benefit.
Although the classical staircase methods are still widely used, e.g. in quantitative sensory testing, the use of more advanced estimation methods that make better use of the acquired information across trials is steadily increasing. In the case of the maximum likelihood estimation method QUEST4,5 used here, this is probably due to the readily available implementation in the popular Matlab PsychToolbox6 suite. The modern, revised version of this procedure is superior to classical estimation methods both in robustness and the low number of trials required to arrive at a sufficient estimate, if used with the right settings7.
The rationale behind the QUEST procedure is to fit a Weibull function to the incoming data to model the psychophysical transformation between stimulus intensity and sensation magnitude. The parameters for the psychophysical Weibull function are in part given by the experimenter, e.g. the steepness of the function or the offset due to the false positive rate and responder inconsistency. The positioning of the parameter of interest along the intensity dimension is approximated by the procedure using Bayesian maximum likelihood estimation. Hereby, a probability distribution is assumed over the location of the target parameter, i.e. the threshold intensity. Given a sensible prior assumption for such a distribution, the algorithm will determine the most informative intensity that the participant should respond to. For the current implementation of the procedure, this is the mean of the prior probability distribution8. For each successive trial, the prior probability distribution is in essence multiplied with the likelihood of the participant&#39;s given response at the tested stimulation level, as characterized by the Weibull function. Every response will be used to continuously update the probability distribution estimate for the threshold parameter. This procedure is repeated until a satisfying estimate is produced. The procedure is more efficient than a simple regression because it makes immediate use of the collected responses to determine which stimulation intensity to test next. Also, the procedure will specifically probe around the point of interest, e.g. a threshold or certain sensation intensity. Using only testing data from such a limited range in regression would lead to an unstable estimate, making adaptive procedures more robust in settings where only relatively low numbers of trials are feasible.
Such robust psychophysical anchoring can be used to measure changes in pain sensitivity over time, modulatory effects in hyperalgesia/allodynia research or analgesic effects in pharmacological interventions, amongst other settings. Another interesting prospect of being able to anchor stimuli to the intensity just at the threshold between two sensory continua is to examine subjective perception across the transition from non-painful to painful sensation9,10,11. This scenario is very interesting because if the pain threshold has been robustly estimated, pain and no-pain conditions can be contrasted in electroencephalographic (EEG) activity, for example, without changing the physical stimulus intensity12. This allows for the observation of pain-specific perceptual processes under constant stimulus conditions by examining the difference in brain activity between trials rated as painful and non-painful.
We will demonstrate how to use the readily available implementation of adaptive estimation in PsychToolbox to robustly determine the individual pain threshold in an EEG experiment where the contrast between pain and no-pain activity is examined for lateralization effects, depending on the stimulation site. Since the stimulation intensity can be kept constant after the thresholding procedure, it is not necessary to account for EEG activity co-varying with stimulus intensity in the subsequent analysis.

<table border="0" cellpadding="0" cellspacing="0" width="835">
	<tbody>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">EasyCap electrode cap</td>
			<td style="width:241px;">EasyCap, Woerthsee-Etterschlag, Germany</td>
			<td style="width:211px;">CUCHW-58</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">actiCap active Ag/Cl EEG electrode set</td>
			<td style="width:241px;">BrainProducts GmbH, Gliching, Germany</td>
			<td style="width:211px;">-</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">SuperVisc EEG eletrode gel</td>
			<td style="width:241px;">EasyCap, Woerthsee-Etterschlag, Germany</td>
			<td style="width:211px;">V16</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">BrainAmp EEG amplifier</td>
			<td style="width:241px;">BrainProducts GmbH, Gliching, Germany</td>
			<td style="width:211px;">BrainAmp Standard</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">PsychToolbox-3</td>
			<td style="width:241px;">Mario Kleiner / Open Source</td>
			<td style="width:211px;">-</td>
			<td>Available at http://psychtoolbox.org/</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Matlab</td>
			<td style="width:241px;">MathWorks, Natick, MA</td>
			<td style="width:211px;">Matlab R2015b</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">DigiTimer DS7A constant current electrical stimulator</td>
			<td style="width:241px;">DigiTimer Ltd., Hertfordshire, United Kingdom</td>
			<td style="width:211px;">DS7A</td>
			<td></td>
		</tr>
	</tbody>
</table>

psychophysically-anchored, robust thresholding in studying pain-related lateralization of oscillatory prestimulus activity

In perceptual studies, it is often important to objectively assess the equality of delivered stimulation across participants or to quantify the intra-individual sensation magnitude that is evoked by stimulation over multiple trials. This requires a robust mapping of stimulus magnitude to perceived intensity and is commonly achieved by psychophysical estimation methods such as the staircase procedure. Newer, more efficient procedures like the QUEST algorithm fit a psychophysical function to the data in real time while at the same time maximizing the efficiency of data collection. A robust estimate of the threshold intensity between painful and nonpainful perceptions can then be used to reduce the influence of variations in sensory input in subsequent analyses of oscillatory brain activity. By stimulating at a constant threshold intensity determined by an adaptive estimation procedure, the variance in the ratings can be directly attributed to perceptual processes. Oscillatory activity can then be contrasted between &#34;pain&#34; and &#34;no-pain&#34; trials directly, yielding activity that closely relates to perceptual classification processes in nociception.

Using a rating scale split into one half for nonpainful and one half for painful sensations (Figure 1a), constant stimulation can be applied over many trials while still yielding ratings across the scale midpoint (Figure 1b). This way, changes in sensory input can be avoided, and the rating outcome can be directly related to intrinsic perceptual classification processes related to pain.
<img alt="Figure 1" src="/files/ftp_upload/55228/55228fig1.jpg" /> 
Figure 1: Experimental Description. (a) The rating scale with the left side spanning non-painful sensation and the right side spanning painful sensation. (b) Procedure used for data collection. 40 thresholding trials followed by 4 - 6 blocks of constant stimulation (30 trials each). The blocks had a jittered 3 - 5 s intertrial interval (ITI). The rating scale appeared 0.25 s after stimulation. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55228/55228fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The two estimation runs starting from the &#34;nonpain&#34; low-point and &#34;pain&#34; high-point converge on robust threshold estimates. Taking the mean of both estimates yields the final threshold estimate, while the bias induced by the starting intensity is reduced (Figure 2a). The subjective stimulation intensity evoked by repeated stimulation at the estimated threshold is stable across multiple blocks of 30 trials each within one experimental session (Figure 2b).
<img alt="Figure 2" src="/files/ftp_upload/55228/55228fig2.jpg" /> 
Figure 2: Stability of the Threshold Estimates. (a) Data for a single participant showing the algorithm converging on two estimates, one for a high intensity starting point, one for a low intensity starting point. To minimize the influence of the starting point, both threshold estimates were averaged (dashed line). (b) Stability of the rating medians over the course of the experiment under constant stimulation at the estimated threshold across all participants (n=25). <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55228/55228fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
By splitting the concurrently recorded EEG data into trials that were rated as &#34;painful&#34; and &#34;nonpainful&#34;, respectively, the oscillatory activity can be contrasted post-hoc. This yields activity differences which coincide with perceptual decisions about the same stimulus being categorized as strong sensation or as pain. Figures 3a and b show these differences for a time-window before the painful stimulus is presented (-0.8 s to 0 s before stimulus onset) and the theta-band frequency range (4 - 7&#160;Hz), which have previously been shown to be connected to subsequent perceptual classification in pain17. The thresholding paradigm enables the examination of such prestimulus differences in oscillatory activity linked to subsequent perceptual classification of pain, independent of stimulus magnitude.
<img alt="Figure 3" src="/files/ftp_upload/55228/55228fig3.jpg" /> 
Figure 3: Power Differences between Nonpain and Pain. Data has been transformed to time-frequency domain using a multi-taper method. Depicted are Theta frequencies between 4 - 7 Hz and before stimulus onset (-0.8 s - 0 s). (a) Power difference specific to the subsequent classification of the stimulus to the left hand as painful. Data adopted from Taesler &#38; Rose17 (n = 15). (b) Power specific to classification of a stimulus to the right hand as painful (n = 10). (c) Common Theta activity between (a) and (b), independent of the stimulated side (n = 25). The topo-plot shows the sum of the lateralized differences between painful and non-painful stimulation. For individual pain/no-pain topographies (S2) as well as a comparison to pre-existing post-stimulus data10 (S3) please refer to the supplemental materials. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55228/55228fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
By changing the side of the stimulation between groups, these pre-stimulus effects can further be disentangled from any lateralization effects in stimulus expectation. Figure 3c shows the sum of activity across both groups (left hand/right hand), highlighting prestimulus theta activity, that is common to the perceptual classification of non-pain versus pain irrespective of the site of stimulation.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Scale position</td>
			<td>Instruction</td>
			<td>Score</td>
		</tr>
		<tr>
			<td>Leftmost</td>
			<td>&#8220;No sensation at all&#8221;</td>
			<td>0</td>
		</tr>
		<tr>
			<td>Left to center</td>
			<td>&#8220;The strongest sensation, that is not yet painful&#8221;</td>
			<td>49</td>
		</tr>
		<tr>
			<td>Center</td>
			<td>&#8220;Pain threshold - this point cannot be selected&#8221;</td>
			<td>50</td>
		</tr>
		<tr>
			<td>Right to center</td>
			<td>&#8220;Painful sensation&#8221;</td>
			<td>51</td>
		</tr>
		<tr>
			<td>Rightmost</td>
			<td>&#8220;Maximum tolerable pain&#8221;</td>
			<td>100</td>
		</tr>
	</tbody>
</table>
Table 1: Definition of Rating Scale Anchor Points. Since the middle of the scale cannot be chosen, the ratings can also be dichotomized into a two-alternate-forced-choice (2AFC) dataset between nonpain and pain.

Watch this Scientific Journal Video about Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity at JoVE.com

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity

Psychophysical methods such as the QUEST estimation procedure can efficiently yield robust estimates of the stimulation intensity at which nonpainful sensations transition into painful sensations. By stimulating repeatedly at the threshold intensity, the variability in rating responses can directly be attributed to perceptual classifications in subsequent analyses.

In perceptual studies, it is often important to objectively assess the equality of delivered stimulation across participants or to quantify the ...

psychophysically-anchored-robust-thresholding-studying-pain-related

Nanyang Technological University

Research

JoVE Journal

Behavior

6.9K Views.  University Medical Center Hamburg Eppendorf. Psychophysical methods such as the QUEST estimation procedure can efficiently yield robust estimates of the stimulation intensity at which nonpainful sensations transition into painful sensations. By stimulating repeatedly at the threshold intensity, the variability in rating responses can directly be attributed to perceptual classifications in subsequent analyses.

Video: Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity

Studying Food Reward and Motivation in Humans

A key challenge in studying reward processing in humans is to go beyond subjective self-report measures and quantify different aspects of reward such as hedonics, motivation, and goal value in more objective ways. This is particularly relevant for the understanding of overeating and obesity as well as their potential treatments. In this paper are described a set of measures of food-related motivation using handgrip force as a motivational measure. These methods can be used to examine changes in food related motivation with metabolic (satiety) and pharmacological manipulations and can be used to evaluate interventions targeted at overeating and obesity. However to understand food-related decision making in the complex food environment it is essential to be able to ascertain the reward goal values that guide the decisions and behavioral choices that people make. These values are hidden but it is possible to ascertain them more objectively using metrics such as the willingness to pay and a method for this is described. Both these sets of methods provide quantitative measures of motivation and goal value that can be compared within and between individuals.

This article describes a set of methods for the measurement of food related motivation and food related goal values in humans.

A key challenge in studying reward processing in humans is to go beyond subjective self-report measures and quantify different aspects of reward such as ...

Measurement of Fronto-limbic Activity Using an Emotional Oddball Task in Children with Familial High Risk for Schizophrenia

Adolescence is a critical developmental period where the early symptoms of schizophrenia frequently emerge. First-degree relatives of people with schizophrenia who are at familial high risk (FHR) may show similar cognitive and emotional changes. However, the neurological changes underlying the emergence of these symptoms remain unclear. This study sought to identify differences in frontal, striatal, and limbic regions in children and adolescents with FHR using functional magnetic resonance imaging. Groups of 21 children and adolescents at FHR and 21 healthy controls completed an emotional oddball task that relied on selective attention and the suppression of task-irrelevant emotional information. The standard oddball task was modified to include aversive and neutral distractors in order to examine potential group differences in both emotional and executive processing. This task was designed specifically to allow for children and adolescents to complete by keeping the difficulty and emotional image content age-appropriate. Furthermore, we demonstrate a technique for suitable fMRI registration for children and adolescent participants. This paradigm may also be applied in future studies to measure changes in neural activity in other populations with hypothesized developmental changes in executive and emotional processing.

This paper describes how to use the emotional oddball task and fMRI to measure brain activation in children and adolescents at familial high risk for schizophrenia (FHR).&#160;FMRI was used to measure differences in fronto-striato-limbic regions during an emotional oddball task. Children with FHR exhibited abnormal functional activation during adolescence.

Adolescence is a critical developmental period where the early symptoms of schizophrenia frequently emerge. First-degree relatives of people with ...

Protocol for Studying Extinction of Conditioned Fear in Naturally Cycling Female Rats

Extinction of conditioned fear has been extensively studied in male rodents. Recently, there have been an increasing number of studies indicating that neural mechanisms for certain behavioral tasks and response behaviors are different in females and males. Using females in research studies can represent a challenge because of the variation of gonadal hormones during their estrous cycle. This protocol describes well-established procedures that are useful in investigating the role of estrogen in fear extinction memory consolidation in female rats. Phase of the estrous cycle and exogenous estrogen administration prior to extinction training can influence extinction recall 24 hr later. The vaginal swabbing technique for estrous phase identification described here aids the examination and manipulation of naturally cycling gonadal hormones. The use of this basic rodent model may further delineate the mechanisms by which estrogen can modulate fear extinction memory in females.

Gonadal hormones such as estrogen modulate memory formation in a number of experimental paradigms including fear extinction memory. This protocol describes a set of methods for investigating the influence of gonadal hormones specifically during extinction in naturally cycling females, including estrous cycle monitoring and exogenous estrogen administration.

Extinction of conditioned fear has been extensively studied in male rodents. Recently, there have been an increasing number of studies indicating that ...

The Double-H Maze: A Robust Behavioral Test for Learning and Memory in Rodents

Spatial cognition research in rodents typically employs the use of maze tasks, whose attributes vary from one maze to the next. These tasks vary by their behavioral flexibility and required memory duration, the number of goals and pathways, and also the overall task complexity. A confounding feature in many of these tasks is the lack of control over the strategy employed by the rodents to reach the goal, e.g., allocentric (declarative-like) or egocentric (procedural) based strategies. The double-H maze is a novel water-escape memory task that addresses this issue, by allowing the experimenter to direct the type of strategy learned during the training period. The double-H maze is a transparent device, which consists of a central alleyway with three arms protruding on both sides, along with an escape platform submerged at the extremity of one of these arms.
Rats can be trained using an allocentric strategy by alternating the start position in the maze in an unpredictable manner (see protocol 1; &#167;4.7), thus requiring them to learn the location of the platform based on the available allothetic cues. Alternatively, an egocentric learning strategy (protocol 2; &#167;4.8) can be employed by releasing the rats from the same position during each trial, until they learn the procedural pattern required to reach the goal. This task has been proven to allow for the formation of stable memory traces.
Memory can be probed following the training period in a misleading probe trial, in which the starting position for the rats alternates. Following an egocentric learning paradigm, rats typically resort to an allocentric-based strategy, but only when their initial view on the extra-maze cues differs markedly from their original position. This task is ideally suited to explore the effects of drugs/perturbations on allocentric/egocentric memory performance, as well as the interactions between these two memory systems.

The goal of this protocol is to investigate spatial cognition in rodents. The double-H water maze is a novel test, which is particularly useful to elucidate the different components of learning, consolidation and memory, as well as the interplay of memory systems.

Spatial cognition research in rodents typically employs the use of maze tasks, whose attributes vary from one maze to the next. These tasks vary by their ...

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

An evaluation method that includes continuous activities in a daily-living environment was developed for Wearable Mobility Monitoring Systems (WMMS) that attempt to recognize user activities. Participants performed a pre-determined set of daily living actions within a continuous test circuit that included mobility activities (walking, standing, sitting, lying, ascending/descending stairs), daily living tasks (combing hair, brushing teeth, preparing food, eating, washing dishes), and subtle environment changes (opening doors, using an elevator, walking on inclines, traversing staircase landings, walking outdoors). 
To evaluate WMMS performance on this circuit, fifteen able-bodied participants completed the tasks while wearing a smartphone at their right front pelvis. The WMMS application used smartphone accelerometer and gyroscope signals to classify activity states. A gold standard comparison data set was created by video-recording each trial and manually logging activity onset times. Gold standard and WMMS data were analyzed offline. Three classification sets were calculated for each circuit: (i) mobility or immobility, ii) sit, stand, lie, or walking, and (iii) sit, stand, lie, walking, climbing stairs, or small standing movement. Sensitivities, specificities, and F-Scores for activity categorization and changes-of-state were calculated.
The mobile versus immobile classification set had a sensitivity of 86.30% &#177; 7.2% and specificity of 98.96% &#177; 0.6%, while the second prediction set had a sensitivity of 88.35% &#177; 7.80% and specificity of 98.51% &#177; 0.62%. For the third classification set, sensitivity was 84.92% &#177; 6.38% and specificity was 98.17 &#177; 0.62. F1 scores for the first, second and third classification sets were 86.17 &#177; 6.3, 80.19 &#177; 6.36, and 78.42 &#177; 5.96, respectively. This demonstrates that WMMS performance depends on the evaluation protocol in addition to the algorithms. The demonstrated protocol can be used and tailored for evaluating human activity recognition systems in rehabilitation medicine where mobility monitoring may be beneficial in clinical decision-making.

A standardized evaluation method was developed for Wearable Mobility Monitoring Systems (WMMS) that includes continuous activities in a realistic daily living environment. Testing with a series of daily living activities can decrease activity recognition sensitivity; therefore, realistic testing circuits are encouraged for valid evaluation of WMMS performance.

An evaluation method that includes continuous activities in a daily-living environment was developed for Wearable Mobility Monitoring Systems (WMMS) that ...

Assessing Activity-based Anorexia in Mice

Rodents develop activity-based anorexia (ABA) when exposed to a restricted feeding schedule and allowed free access to a running wheel. These conditions lead to a life-threatening reduction in body weight. However, rodents exposed to only one of these conditions ultimately adapt to re-establish normal body weight. Although increased running coupled with reduction in voluntary food intake appear paradoxical under ABA conditions, ABA behavior is observed across numerous mammalian species. 
The ABA paradigm provides an animal model for anorexia nervosa (AN), an eating disorder with severe dysregulation of appetite-behavior. Subjects are singly housed with free access to a running wheel. Each day, the subject is offered food for a limited amount of time. During the course of the experiment, a subject's body weight decreases from high activity and low caloric intake. The duration of the study varies based on how long food is offered daily, the type of food offered, the strain of mouse, if drugs are being tested, and environmental factors.
A lack of effective pharmacological treatments for AN patients, their low quality of life, high cost of treatment, and their high mortality rate indicate the urgency to further research AN. We provide a basic outline for performing ABA experiments with mice, offering a method to investigate AN-like behavior in order to develop novel therapies. This protocol is optimized for use in Balb/cJ mice, but can easily be manipulated for other strains, providing great flexibility in working with different questions, especially related to genetic factors of ABA.

Mice individually housed with a running wheel while given limited access to food develop reductions in food consumption and increase activity on the running wheel. This experimental phenomenon is called activity-based anorexia. This paradigm provides an experimental tool for studying the neurobiology and behaviors underlying aspects of anorexia nervosa.

Rodents develop activity-based anorexia (ABA) when exposed to a restricted feeding schedule and allowed free access to a running wheel. These conditions ...

Stress-Enhanced Fear Learning, a Robust Rodent Model of Post-Traumatic Stress Disorder

Fear behaviors are important for survival, but disproportionately high levels of fear can increase the vulnerability for developing psychiatric disorders such as post-traumatic stress disorder (PTSD). To understand the biological mechanisms of fear dysregulation in PTSD, it is important to start with a valid animal model of the disorder. This protocol describes the methodology required to conduct stress-enhanced fear learning (SEFL) experiments, a preclinical model of PTSD, in both rats and mice. SEFL was developed to recapitulate critical aspects of PTSD, including long-term sensitization of fear learning caused by an acute stressor. SEFL uses aspects of Pavlovian fear conditioning but produces a distinct and robust sensitized fear response far greater than normal conditional fear responses. The trauma procedure involves placing a rodent in a conditioning chamber and administering 15 unsignaled shocks randomly distributed over 90 minutes (for rat experiments; for mouse experiments, 10 unsignaled shocks randomly distributed over 60 minutes are used). On day 2, rodents are placed in a novel conditioning context where they receive a single shock; then, on day 3 they are placed back in the same context as on day 2 and tested for changes in freezing levels. Rodents that previously received the trauma display enhanced levels of freezing on the test day compared to those that received no shocks on the first day. Thus, with this model, a single highly stressful experience (the trauma) produces extreme fear of the stimuli associated with the traumatic event.

Here we describe the detailed methodology required to conduct stress-enhanced fear learning (SEFL) experiments, a preclinical model of post-traumatic stress disorder, in both rats and mice. The model utilizes aspects of Pavlovian fear conditioning and freezing as an index of enhanced fear in rodents.

Fear behaviors are important for survival, but disproportionately high levels of fear can increase the vulnerability for developing psychiatric disorders ...

A Visual Guide for Studying Behavioral Defenses to Pathogen Attacks in Leaf-Cutting Ants

The complex lifestyle, evolutionary history of advanced cooperation, and disease defenses of leaf-cutting ants are well studied. Although numerous studies have described the behaviors connected with disease defense, and the associated use of chemicals and antimicrobials, no common visual reference has been made. The main aim of this study was to record short clips of behaviors involved in disease defense, both prophylactically and directly targeted towards an antagonist of the colony following infection. To do so we used an infection experiment, with sub-colonies of the leaf-cutting ant species Acromyrmex echinatior, and the most significant known pathogenic threat to the ants&#39; fungal crop (Leucoagaricus gongylophorus), a specialized pathogenic fungus in the genus Escovopsis. We filmed and compared infected and uninfected colonies, at both early and more advanced stages of infection. We quantified key defensive behaviors across treatments and show that the behavioral response to pathogen attack likely varies between different castes of worker ants, and between early and late detection of a threat. Based on these recordings we have made a library of behavioral clips, accompanied by definitions of the main individual defensive behaviors. We anticipate that such a guide can provide a common frame of reference for other researchers working in this field, to recognize and study these behaviors, and also provide greater scope for comparing different studies to ultimately help better understand the role these behaviors play in disease defense.

We present a visual guide to disease defense behaviors in leaf-cutting ants, with individual clips and accompanying definitions, illustrated in an experimental infection scenario. Our main aim is to help other researchers recognize key defensive behaviors and to provide a common understanding for future research in this field.

The complex lifestyle, evolutionary history of advanced cooperation, and disease defenses of leaf-cutting ants are well studied. Although numerous studies ...

Behavioral Approaches to Studying Innate Stress in Zebrafish

Responding appropriately to stressful stimuli is essential for survival of an organism. Extensive research has been done on a wide spectrum of stress-related diseases and psychiatric disorders, yet further studies into the genetic and neuronal regulation of stress are still required to develop better therapeutics. The zebrafish provides a powerful genetic model to investigate the neural underpinnings of stress, as there exists a large collection of mutant and transgenic lines. Moreover, pharmacology can easily be applied to zebrafish, as most drugs can be added directly to water. We describe here the use of the 'novel tank test' as a method to study innate stress responses in zebrafish, and demonstrate how potential anxiolytic drugs can be validated using the assay. The method can easily be coupled with zebrafish lines harboring genetic mutations, or those in which transgenic approaches for manipulating precise neural circuits are used. The assay can also be used in other fish models. Together, the described protocol should facilitate the adoption of this simple assay to other laboratories.

This manuscript describes a simple method to measure stress behaviorally in adult zebrafish. The approach takes advantage of the innate tendency that zebrafish prefer the bottom half of a tank when in a stressful state. We also describe methods for coupling the assay with pharmacology.

Responding appropriately to stressful stimuli is essential for survival of an organism. Extensive research has been done on a wide spectrum of ...

Automated Measurements of Sleep and Locomotor Activity in Mexican Cavefish

Across phyla, sleep is characterized by highly conserved behavioral characteristics that include elevated arousal threshold, rebound following sleep deprivation, and consolidated periods of behavioral immobility. The Mexican cavefish, Astyanax mexicanus (A. mexicanus), is a model for studying trait evolution in response to environmental perturbation. A. mexicanus exist as in eyed surface-dwelling forms and multiple blind cave-dwelling populations that have robust morphological and behavioral differences. Sleep loss has occurred in multiple, independently-evolved cavefish populations. This protocol describes a methodology for quantifying sleep and locomotor activity in A. mexicanus cave and surface fish. A cost-effective video monitoring system allows for behavioral imaging of individually-housed larval or adult fish for periods of a week or longer. The system can be applied to fish aged 4 days post fertilization through adulthood. The approach can also be adapted for measuring the effects of social interactions on sleep by recording multiple fish in a single arena. Following behavioral recordings, data is analyzed using automated tracking software and sleep analysis is processed using customized scripts that quantify multiple sleep variables including duration, bout length, and bout number. This system can be applied to measure sleep, circadian behavior, and locomotor activity in almost any fish species including zebrafish and sticklebacks.

This protocol details the methodology for quantifying locomotor behavior and sleep in the Mexican cavefish. Previous analyses are extended to measure these behaviors in socially-housed fish. This system can be widely applied to study sleep and activity in other fish species.

Across phyla, sleep is characterized by highly conserved behavioral characteristics that include elevated arousal threshold, rebound following sleep ...